# Gradio YOLOv5 Det v0.5
# 创建人：曾逸夫
# 创建时间：2022-06-03

import os

os.system("pip install gradio==3.1.3")

import argparse
import csv
import sys

csv.field_size_limit(sys.maxsize)

import gc
import json
import random
from collections import Counter
from pathlib import Path

import cv2
import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.express as px
from matplotlib import font_manager

ROOT_PATH = sys.path[0]  # 项目根目录

# --------------------- 字体库 ---------------------
SimSun_path = f"{ROOT_PATH}/fonts/SimSun.ttf"  # 宋体文件路径
TimesNesRoman_path = f"{ROOT_PATH}/fonts/TimesNewRoman.ttf"  # 新罗马字体文件路径
# 宋体
SimSun = font_manager.FontProperties(fname=SimSun_path, size=12)
# 新罗马字体
TimesNesRoman = font_manager.FontProperties(fname=TimesNesRoman_path, size=12)

import torch
import yaml
from PIL import Image, ImageDraw, ImageFont

from util.fonts_opt import is_fonts
from util.pdf_opt import pdf_generate

ROOT_PATH = sys.path[0]  # 根目录

# yolov5路径
yolov5_path = "ultralytics/yolov5"

# 本地模型路径
local_model_path = f"{ROOT_PATH}/models"

# Gradio YOLOv5 Det版本
GYD_VERSION = "Gradio YOLOv5 Det v0.5"

# 模型名称临时变量
model_name_tmp = ""

# 设备临时变量
device_tmp = ""

# 文件后缀
suffix_list = [".csv", ".yaml"]

# 字体大小
FONTSIZE = 25

# 目标尺寸
obj_style = ["小目标", "中目标", "大目标"]


def parse_args(known=False):
    parser = argparse.ArgumentParser(description="Gradio YOLOv5 Det v0.5")
    parser.add_argument("--source", "-src", default="upload", type=str, help="image input source")
    parser.add_argument("--source_video", "-src_v", default="upload", type=str, help="video input source")
    parser.add_argument("--img_tool", "-it", default="editor", type=str, help="input image tool")
    parser.add_argument("--model_name", "-mn", default="yolov5s", type=str, help="model name")
    parser.add_argument(
        "--model_cfg",
        "-mc",
        default="./model_config/model_name_p5_p6_all.yaml",
        type=str,
        help="model config",
    )
    parser.add_argument(
        "--cls_name",
        "-cls",
        default="./cls_name/cls_name_zh.yaml",
        type=str,
        help="cls name",
    )
    parser.add_argument(
        "--nms_conf",
        "-conf",
        default=0.5,
        type=float,
        help="model NMS confidence threshold",
    )
    parser.add_argument("--nms_iou", "-iou", default=0.45, type=float, help="model NMS IoU threshold")
    parser.add_argument(
        "--device",
        "-dev",
        default="cuda:0",
        type=str,
        help="cuda or cpu",
    )
    parser.add_argument("--inference_size", "-isz", default=640, type=int, help="model inference size")
    parser.add_argument("--max_detnum", "-mdn", default=50, type=float, help="model max det num")
    parser.add_argument("--slider_step", "-ss", default=0.05, type=float, help="slider step")
    parser.add_argument(
        "--is_login",
        "-isl",
        action="store_true",
        default=False,
        help="is login",
    )
    parser.add_argument('--usr_pwd',
                        "-up",
                        nargs='+',
                        type=str,
                        default=["admin", "admin"],
                        help="user & password for login")
    parser.add_argument(
        "--is_share",
        "-is",
        action="store_true",
        default=False,
        help="is login",
    )

    args = parser.parse_known_args()[0] if known else parser.parse_args()
    return args


# yaml文件解析
def yaml_parse(file_path):
    return yaml.safe_load(open(file_path, encoding="utf-8").read())


# yaml csv 文件解析
def yaml_csv(file_path, file_tag):
    file_suffix = Path(file_path).suffix
    if file_suffix == suffix_list[0]:
        # 模型名称
        file_names = [i[0] for i in list(csv.reader(open(file_path)))]  # csv版
    elif file_suffix == suffix_list[1]:
        # 模型名称
        file_names = yaml_parse(file_path).get(file_tag)  # yaml版
    else:
        print(f"{file_path}格式不正确！程序退出！")
        sys.exit()

    return file_names


# 检查网络连接
def check_online():
    # 参考：https://github.com/ultralytics/yolov5/blob/master/utils/general.py
    # Check internet connectivity
    import socket
    try:
        socket.create_connection(("1.1.1.1", 443), 5)  # check host accessibility
        return True
    except OSError:
        return False


#  模型加载
def model_loading(model_name, device, opt=[]):

    # 加载本地模型
    try:
        torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
        model = torch.hub.load(
            yolov5_path,
            "custom",
            path=f"{local_model_path}/{model_name}",
            device=device,
            force_reload=[True if "refresh_yolov5" in opt and check_online() else False][0],
            _verbose=True,
        )
    except Exception as e:
        print("模型加载失败！")
        print(e)
        return False
    else:
        print(f"🚀 欢迎使用{GYD_VERSION}，{model_name}加载成功！")

    return model


# 检测信息
def export_json(results, img_size):

    return [[{
        "ID": i,
        "CLASS": int(result[i][5]),
        "CLASS_NAME": model_cls_name_cp[int(result[i][5])],
        "BOUNDING_BOX": {
            "XMIN": round(result[i][:4].tolist()[0], 6),
            "YMIN": round(result[i][:4].tolist()[1], 6),
            "XMAX": round(result[i][:4].tolist()[2], 6),
            "YMAX": round(result[i][:4].tolist()[3], 6),},
        "CONF": round(float(result[i][4]), 2),
        "FPS": round(1000 / float(results.t[1]), 2),
        "IMG_WIDTH": img_size[0],
        "IMG_HEIGHT": img_size[1],} for i in range(len(result))] for result in results.xyxyn]


# 标签和边界框颜色设置
def color_set(cls_num):
    color_list = []
    for i in range(cls_num):
        color = tuple(np.random.choice(range(256), size=3))
        # color = ["#"+''.join([random.choice('0123456789ABCDEF') for j in range(6)])]
        color_list.append(color)

    return color_list


# 随机生成浅色系或者深色系
def random_color(cls_num, is_light=True):
    color_list = []
    for i in range(cls_num):
        color = (
            random.randint(0, 127) + int(is_light) * 128,
            random.randint(0, 127) + int(is_light) * 128,
            random.randint(0, 127) + int(is_light) * 128,
        )
        color_list.append(color)

    return color_list


# 检测绘制
def pil_draw(img, score_l, bbox_l, cls_l, cls_index_l, textFont, color_list, opt):

    img_pil = ImageDraw.Draw(img)
    id = 0

    for score, (xmin, ymin, xmax, ymax), label, cls_index in zip(score_l, bbox_l, cls_l, cls_index_l):

        img_pil.rectangle([xmin, ymin, xmax, ymax], fill=None, outline=color_list[cls_index], width=2)  # 边界框
        countdown_msg = f"{id}-{label} {score:.2f}"
        text_w, text_h = textFont.getsize(countdown_msg)  # 标签尺寸
        if "label" in opt:
            # 标签背景
            img_pil.rectangle(
                (xmin, ymin, xmin + text_w, ymin + text_h),
                fill=color_list[cls_index],
                outline=color_list[cls_index],
            )

            # 标签
            img_pil.multiline_text(
                (xmin, ymin),
                countdown_msg,
                fill=(0, 0, 0),
                font=textFont,
                align="center",
            )

        id += 1

    return img


# YOLOv5图片检测函数
def yolo_det_img(img, device, model_name, infer_size, conf, iou, max_num, model_cls, opt):

    global model, model_name_tmp, device_tmp

    if img is None or img == "":
        # 判断是否有图片存在
        print("图片不存在！")
        return None, None, None, None, None, None, None

    det_img = img.copy()
    # 目标尺寸个数
    s_obj, m_obj, l_obj = 0, 0, 0

    area_obj_all = []  # 目标面积

    score_det_stat = []  # 置信度统计
    bbox_det_stat = []  # 边界框统计
    cls_det_stat = []  # 类别数量统计
    cls_index_det_stat = []  # 类别索引统计

    pdf_csv_xlsx = []  # 文件生成列表

    if model_name_tmp != model_name:
        # 模型判断，避免反复加载
        model_name_tmp = model_name
        print(f"正在加载模型{model_name_tmp}......")
        model = model_loading(model_name_tmp, device, opt)
    elif device_tmp != device:
        # 设备判断，避免反复加载
        device_tmp = device
        print(f"正在加载模型{model_name_tmp}......")
        model = model_loading(model_name_tmp, device, opt)
    else:
        print(f"正在加载模型{model_name_tmp}......")
        model = model_loading(model_name_tmp, device, opt)

    # ----------- 模型调参 -----------
    model.conf = conf  # NMS 置信度阈值
    model.iou = iou  # NMS IoU阈值
    model.max_det = int(max_num)  # 最大检测框数
    model.classes = model_cls  # 模型类别

    color_list = random_color(len(model_cls_name_cp), True)

    img_size = img.size  # 帧尺寸

    results = model(img, size=infer_size)  # 检测
    # 判断检测对象是否为空
    # 参考：https://gitee.com/CV_Lab/face-labeling/blob/master/face_labeling.py
    is_results_null = results.xyxyn[0].shape == torch.Size([0, 6])

    if not is_results_null:

        # ---------------- 目标裁剪 ----------------
        crops = results.crop(save=False)
        img_crops = []
        for i in range(len(crops)):
            img_crops.append(crops[i]["im"][..., ::-1])

        # 数据表
        dataframe = results.pandas().xyxy[0].round(2)

        report = "./Det_Report.pdf"
        det_csv = "./Det_Report.csv"
        det_excel = "./Det_Report.xlsx"

        if "csv" in opt:
            dataframe.to_csv(det_csv, index=False)
            pdf_csv_xlsx.append(det_csv)
        else:
            det_csv = None

        if "excel" in opt:
            dataframe.to_excel(det_excel, sheet_name='sheet1', index=False)
            pdf_csv_xlsx.append(det_excel)
        else:
            det_excel = None

        # ---------------- 加载字体 ----------------
        yaml_index = cls_name.index(".yaml")
        cls_name_lang = cls_name[yaml_index - 2:yaml_index]

        if cls_name_lang == "zh":
            # 中文
            textFont = ImageFont.truetype(str(f"{ROOT_PATH}/fonts/SimSun.ttf"), size=FONTSIZE)
        elif cls_name_lang in ["en", "ru", "es", "ar"]:
            # 英文、俄语、西班牙语、阿拉伯语
            textFont = ImageFont.truetype(str(f"{ROOT_PATH}/fonts/TimesNewRoman.ttf"), size=FONTSIZE)
        elif cls_name_lang == "ko":
            # 韩语
            textFont = ImageFont.truetype(str(f"{ROOT_PATH}/fonts/malgun.ttf"), size=FONTSIZE)

        for result in results.xyxyn:
            for i in range(len(result)):
                # id = int(i)  # 实例ID
                obj_cls_index = int(result[i][5])  # 类别索引
                cls_index_det_stat.append(obj_cls_index)

                obj_cls = model_cls_name_cp[obj_cls_index]  # 类别
                cls_det_stat.append(obj_cls)

                # ------------ 边框坐标 ------------
                x0 = float(result[i][:4].tolist()[0])
                y0 = float(result[i][:4].tolist()[1])
                x1 = float(result[i][:4].tolist()[2])
                y1 = float(result[i][:4].tolist()[3])

                # ------------ 边框实际坐标 ------------
                x0 = int(img_size[0] * x0)
                y0 = int(img_size[1] * y0)
                x1 = int(img_size[0] * x1)
                y1 = int(img_size[1] * y1)
                bbox_det_stat.append((x0, y0, x1, y1))

                conf = float(result[i][4])  # 置信度
                score_det_stat.append(conf)

                # fps = f"{(1000 / float(results.t[1])):.2f}"  # FPS

                # ---------- 加入目标尺寸 ----------
                w_obj = x1 - x0
                h_obj = y1 - y0
                area_obj = w_obj * h_obj
                area_obj_all.append(area_obj)

        det_img = pil_draw(img, score_det_stat, bbox_det_stat, cls_det_stat, cls_index_det_stat, textFont, color_list,
                           opt)
        # ------------ JSON生成 ------------
        det_json = export_json(results, img.size)[0]  # 检测信息
        det_json_format = json.dumps(det_json, sort_keys=False, indent=4, separators=(",", ":"),
                                     ensure_ascii=False)  # JSON格式化
        if "json" not in opt:
            det_json = None

        # -------------- PDF生成 --------------
        if "pdf" in opt:
            pdf_generate(f"{det_json_format}", report, GYD_VERSION)
            pdf_csv_xlsx.append(report)
        else:
            report = None

        # -------------- 目标尺寸计算 --------------
        for i in range(len(area_obj_all)):
            if (0 < area_obj_all[i] <= 32 ** 2):
                s_obj = s_obj + 1
            elif (32 ** 2 < area_obj_all[i] <= 96 ** 2):
                m_obj = m_obj + 1
            elif (area_obj_all[i] > 96 ** 2):
                l_obj = l_obj + 1

        sml_obj_total = s_obj + m_obj + l_obj
        objSize_dict = {}
        objSize_dict = {obj_style[i]: [s_obj, m_obj, l_obj][i] / sml_obj_total for i in range(3)}

        # ------------ 类别统计 ------------
        clsRatio_dict = {}
        clsDet_dict = Counter(cls_det_stat)
        clsDet_dict_sum = sum(clsDet_dict.values())
        for k, v in clsDet_dict.items():
            clsRatio_dict[k] = v / clsDet_dict_sum

        return det_img, img_crops, objSize_dict, clsRatio_dict, dataframe, det_json, pdf_csv_xlsx
    else:
        print("图片目标不存在！")
        return None, None, None, None, None, None, None


# YOLOv5视频检测函数
def yolo_det_video(video, device, model_name, infer_size, conf, iou, max_num, model_cls, opt, draw_style):

    global model, model_name_tmp, device_tmp

    if video is None or video == "":
        # 判断是否有图片存在
        print("视频不存在！")
        return None, None, None

    # 目标尺寸个数
    s_obj, m_obj, l_obj = 0, 0, 0

    area_obj_all = []  # 目标面积
    s_list, m_list, l_list = [], [], []

    score_det_stat = []  # 置信度统计
    bbox_det_stat = []  # 边界框统计
    cls_det_stat = []  # 类别数量统计
    cls_index_det_stat = []  # 类别索引统计

    fps_list = []

    frame_count = 0  # 帧数
    fps = 0  # FPS

    os.system("""
        if [ -e './output.mp4' ]; then
        rm ./output.mp4
        fi
        """)

    if model_name_tmp != model_name:
        # 模型判断，避免反复加载
        model_name_tmp = model_name
        print(f"正在加载模型{model_name_tmp}......")
        model = model_loading(model_name_tmp, device, opt)
    elif device_tmp != device:
        # 设备判断，避免反复加载
        device_tmp = device
        print(f"正在加载模型{model_name_tmp}......")
        model = model_loading(model_name_tmp, device, opt)
    else:
        print(f"正在加载模型{model_name_tmp}......")
        model = model_loading(model_name_tmp, device, opt)

    # ----------- 模型调参 -----------
    model.conf = conf  # NMS 置信度阈值
    model.iou = iou  # NMS IOU阈值
    model.max_det = int(max_num)  # 最大检测框数
    model.classes = model_cls  # 模型类别

    color_list = random_color(len(model_cls_name_cp), True)

    # ---------------- 加载字体 ----------------
    yaml_index = cls_name.index(".yaml")
    cls_name_lang = cls_name[yaml_index - 2:yaml_index]

    if cls_name_lang == "zh":
        # 中文
        textFont = ImageFont.truetype(str(f"{ROOT_PATH}/fonts/SimSun.ttf"), size=FONTSIZE)
    elif cls_name_lang in ["en", "ru", "es", "ar"]:
        # 英文、俄语、西班牙语、阿拉伯语
        textFont = ImageFont.truetype(str(f"{ROOT_PATH}/fonts/TimesNewRoman.ttf"), size=FONTSIZE)
    elif cls_name_lang == "ko":
        # 韩语
        textFont = ImageFont.truetype(str(f"{ROOT_PATH}/fonts/malgun.ttf"), size=FONTSIZE)

    # video->frame
    gc.collect()
    output_video_path = "./output.avi"
    cap = cv2.VideoCapture(video)
    fourcc = cv2.VideoWriter_fourcc(*"I420")  # 编码器

    out = cv2.VideoWriter(output_video_path, fourcc, 30.0, (int(cap.get(3)), int(cap.get(4))))
    if cap.isOpened():
        while cap.isOpened():
            ret, frame = cap.read()
            # 判断空帧
            if not ret:
                break

            frame_count += 1  # 帧数自增

            results = model(frame, size=infer_size)  # 检测

            h, w, _ = frame.shape  # 帧尺寸
            img_size = (w, h)  # 帧尺寸

            for result in results.xyxyn:
                for i in range(len(result)):
                    # id = int(i)  # 实例ID
                    obj_cls_index = int(result[i][5])  # 类别索引
                    cls_index_det_stat.append(obj_cls_index)

                    obj_cls = model_cls_name_cp[obj_cls_index]  # 类别
                    cls_det_stat.append(obj_cls)

                    # ------------边框坐标------------
                    x0 = float(result[i][:4].tolist()[0])
                    y0 = float(result[i][:4].tolist()[1])
                    x1 = float(result[i][:4].tolist()[2])
                    y1 = float(result[i][:4].tolist()[3])

                    # ------------边框实际坐标------------
                    x0 = int(img_size[0] * x0)
                    y0 = int(img_size[1] * y0)
                    x1 = int(img_size[0] * x1)
                    y1 = int(img_size[1] * y1)
                    bbox_det_stat.append((x0, y0, x1, y1))

                    conf = float(result[i][4])  # 置信度
                    score_det_stat.append(conf)

                    fps = f"{(1000 / float(results.t[1])):.2f}"  # FPS

                    # ---------- 加入目标尺寸 ----------
                    w_obj = x1 - x0
                    h_obj = y1 - y0
                    area_obj = w_obj * h_obj
                    area_obj_all.append(area_obj)

            # 判断检测对象是否为空
            # 参考：https://gitee.com/CV_Lab/face-labeling/blob/master/face_labeling.py
            is_results_null = results.xyxyn[0].shape == torch.Size([0, 6])
            if not is_results_null:
                fps_list.append(float(fps))
            else:
                fps_list.append(0.0)

            frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
            frame = pil_draw(frame, score_det_stat, bbox_det_stat, cls_det_stat, cls_index_det_stat, textFont,
                             color_list, opt)
            frame = cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2BGR)

            # frame->video
            out.write(frame)

            # ----- 清空统计列表 -----
            score_det_stat = []
            bbox_det_stat = []
            cls_det_stat = []
            cls_index_det_stat = []

            # -------------- 目标尺寸计算 --------------
            for i in range(len(area_obj_all)):
                if (0 < area_obj_all[i] <= 32 ** 2):
                    s_obj = s_obj + 1
                elif (32 ** 2 < area_obj_all[i] <= 96 ** 2):
                    m_obj = m_obj + 1
                elif (area_obj_all[i] > 96 ** 2):
                    l_obj = l_obj + 1

            s_list.append(s_obj)
            m_list.append(m_obj)
            l_list.append(l_obj)

            # 目标尺寸个数
            s_obj, m_obj, l_obj = 0, 0, 0
            # 目标面积
            area_obj_all = []

        out.release()
        cap.release()
        # cv2.destroyAllWindows()

        df_objSize = pd.DataFrame({"fID": list(range(frame_count))})
        df_objSize[obj_style[0]] = tuple(s_list)
        df_objSize[obj_style[1]] = tuple(m_list)
        df_objSize[obj_style[2]] = tuple(l_list)
        print(df_objSize)

        if draw_style == "Plotly":
            # -------------------- 帧数-目标尺寸数图 --------------------
            fig_objSize = px.scatter(df_objSize, x="fID", y=obj_style)  # 散点图
            # fig_objSize = px.line(df_objSize, x="fID", y=obj_style, markers=True)  # 折线图
            fig_objSize.update_layout(title="帧数-目标尺寸数", xaxis_title="帧数", yaxis_title="目标尺寸数")

            # -------------------- 帧数-FPS图 --------------------
            fig_fps = px.scatter(df_objSize, x="fID", y=fps_list)
            # fig_fps = px.line(df_objSize, x="fID", y=fps_list, markers=True)
            fig_fps.update_layout(title="帧数-FPS", xaxis_title="帧数", yaxis_title="FPS")

        elif draw_style == "Matplotlib":
            # -------------------- 帧数-目标尺寸数图 --------------------
            fig_objSize = plt.figure()

            # -------------------- 散点图 --------------------
            plt.scatter(df_objSize['fID'], df_objSize[obj_style[0]])
            plt.scatter(df_objSize['fID'], df_objSize[obj_style[1]])
            plt.scatter(df_objSize['fID'], df_objSize[obj_style[2]])
            # plt.plot(df_objSize['fID'], df_objSize[obj_style])  # 折线图
            plt.title("帧数-目标尺寸数图", fontsize=12, fontproperties=SimSun)
            plt.xlabel("帧数", fontsize=12, fontproperties=SimSun)
            plt.ylabel("目标尺寸数", fontsize=12, fontproperties=SimSun)
            plt.legend(obj_style, prop=SimSun, fontsize=12, loc="best")

            # -------------------- 帧数-FPS图 --------------------
            fig_fps = plt.figure()

            plt.scatter(df_objSize['fID'], fps_list)
            # plt.plot(df_objSize['fID'], df_objSize[obj_style])  # 折线图
            plt.title("帧数-FPS", fontsize=12, fontproperties=SimSun)
            plt.xlabel("帧数", fontsize=12, fontproperties=SimSun)
            plt.ylabel("FPS", fontsize=12, fontproperties=SimSun)

        return output_video_path, fig_objSize, fig_fps

    else:
        print("视频加载失败！")
        return None, None, None


def main(args):
    gr.close_all()

    global model, model_cls_name_cp, cls_name

    source = args.source
    source_video = args.source_video
    img_tool = args.img_tool
    nms_conf = args.nms_conf
    nms_iou = args.nms_iou
    model_name = args.model_name
    model_cfg = args.model_cfg
    cls_name = args.cls_name
    device = args.device
    inference_size = args.inference_size
    max_detnum = args.max_detnum
    slider_step = args.slider_step
    is_login = args.is_login
    usr_pwd = args.usr_pwd
    is_share = args.is_share

    is_fonts(f"{ROOT_PATH}/fonts")  # 检查字体文件

    # 模型加载
    model = model_loading(model_name, device)

    model_names = yaml_csv(model_cfg, "model_names")  # 模型名称
    model_cls_name = yaml_csv(cls_name, "model_cls_name")  # 类别名称

    model_cls_name_cp = model_cls_name.copy()  # 类别名称

    # ------------------- 图片模式输入组件 -------------------
    inputs_img = gr.Image(image_mode="RGB", source=source, tool=img_tool, type="pil", label="原始图片")
    inputs_device01 = gr.Radio(choices=["cuda:0", "cpu"], value=device, label="设备")
    inputs_model01 = gr.Dropdown(choices=model_names, value=model_name, type="value", label="模型")
    inputs_size01 = gr.Slider(384, 1536, step=128, value=inference_size, label="推理尺寸")
    input_conf01 = gr.Slider(0, 1, step=slider_step, value=nms_conf, label="置信度阈值")
    inputs_iou01 = gr.Slider(0, 1, step=slider_step, value=nms_iou, label="IoU 阈值")
    inputs_maxnum01 = gr.Number(value=max_detnum, label="最大检测数")
    inputs_clsName01 = gr.CheckboxGroup(choices=model_cls_name, value=model_cls_name, type="index", label="类别")
    inputs_opt01 = gr.CheckboxGroup(choices=["refresh_yolov5", "label", "pdf", "json", "csv", "excel"],
                                    value=["label", "pdf"],
                                    type="value",
                                    label="操作")

    # ------------------- 视频模式输入组件 -------------------
    inputs_video = gr.Video(format="mp4", source=source_video, mirror_webcam=False, label="原始视频")  # webcam
    inputs_device02 = gr.Radio(choices=["cuda:0", "cpu"], value=device, label="设备")
    inputs_model02 = gr.Dropdown(choices=model_names, value=model_name, type="value", label="模型")
    inputs_size02 = gr.Slider(384, 1536, step=128, value=inference_size, label="推理尺寸")
    input_conf02 = gr.Slider(0, 1, step=slider_step, value=nms_conf, label="置信度阈值")
    inputs_iou02 = gr.Slider(0, 1, step=slider_step, value=nms_iou, label="IoU 阈值")
    inputs_maxnum02 = gr.Number(value=max_detnum, label="最大检测数")
    inputs_clsName02 = gr.CheckboxGroup(choices=model_cls_name, value=model_cls_name, type="index", label="类别")
    inputs_opt02 = gr.CheckboxGroup(choices=["refresh_yolov5", "label"], value=["label"], type="value", label="操作")
    inputs_draw02 = gr.Radio(choices=["Matplotlib", "Plotly"], value="Matplotlib", label="绘图")

    # ------------------- 图片模式输入参数 -------------------
    inputs_img_list = [
        inputs_img,  # 输入图片
        inputs_device01,  # 设备
        inputs_model01,  # 模型
        inputs_size01,  # 推理尺寸
        input_conf01,  # 置信度阈值
        inputs_iou01,  # IoU阈值
        inputs_maxnum01,  # 最大检测数
        inputs_clsName01,  # 类别
        inputs_opt01,  # 检测操作
    ]

    # ------------------- 视频模式输入参数 -------------------
    inputs_video_list = [
        inputs_video,  # 输入图片
        inputs_device02,  # 设备
        inputs_model02,  # 模型
        inputs_size02,  # 推理尺寸
        input_conf02,  # 置信度阈值
        inputs_iou02,  # IoU阈值
        inputs_maxnum02,  # 最大检测数
        inputs_clsName02,  # 类别
        inputs_opt02,  # 检测操作
        inputs_draw02,  # 绘图操作
    ]

    # ------------------- 图片模式输出组件 -------------------
    outputs_img = gr.Image(type="pil", label="检测图片")
    outputs_df = gr.Dataframe(max_rows=5, overflow_row_behaviour="paginate", type="pandas", label="检测信息列表")
    outputs_crops = gr.Gallery(label="目标裁剪")
    outputs_objSize = gr.Label(label="目标尺寸占比统计")
    outputs_clsSize = gr.Label(label="类别检测占比统计")
    outputs_json = gr.JSON(label="检测信息")
    outputs_pdf = gr.File(label="检测报告")

    # ------------------- 视频模式输出组件 -------------------
    outputs_video = gr.Video(format='mp4', label="检测视频")
    outputs_frame_objSize_plot = gr.Plot(label="帧数-目标尺寸数")
    outputs_frame_fps_plot = gr.Plot(label="帧数-FPS")

    # ------------------- 图片模式输出参数 -------------------
    outputs_img_list = [
        outputs_img, outputs_crops, outputs_objSize, outputs_clsSize, outputs_df, outputs_json, outputs_pdf]

    # ------------------- 视频模式输出参数 -------------------
    outputs_video_list = [outputs_video, outputs_frame_objSize_plot, outputs_frame_fps_plot]

    # 标题
    title = "Gradio YOLOv5 Det v0.5"

    # 描述
    description = "<div align='center'>可自定义目标检测模型、安装简单、使用方便</div>"
    # article="https://gitee.com/CV_Lab/gradio_yolov5_det"

    # 示例图片
    examples_img = [
        [
            "./img_examples/bus.jpg",
            "cpu",
            "yolov5s",
            640,
            0.6,
            0.5,
            10,
            ["人", "公交车"],
            ["label", "pdf"],],
        [
            "./img_examples/giraffe.jpg",
            "cuda:0",
            "yolov5l",
            320,
            0.5,
            0.45,
            12,
            ["长颈鹿"],
            ["label", "pdf"],],
        [
            "./img_examples/zidane.jpg",
            "cuda:0",
            "yolov5m",
            640,
            0.6,
            0.5,
            15,
            ["人", "领带"],
            ["pdf", "json"],],
        [
            "./img_examples/Millenial-at-work.jpg",
            "cuda:0",
            "yolov5s6",
            1280,
            0.5,
            0.5,
            20,
            ["人", "椅子", "杯子", "笔记本电脑"],
            ["label", "pdf", "csv", "excel"],],]

    examples_video = [
        [
            "./video_examples/test01.mp4",
            "cuda:0",
            "yolov5s",
            640,
            0.5,
            0.45,
            12,
            ["鸟"],
            ["label"],
            "Matplotlib",],
        [
            "./video_examples/test02.mp4",
            "cuda:0",
            "yolov5m",
            640,
            0.6,
            0.5,
            15,
            ["马"],
            ["label"],
            "Matplotlib",],
        [
            "./video_examples/test03.mp4",
            "cuda:0",
            "yolov5s6",
            1280,
            0.5,
            0.5,
            20,
            ["人", "风筝"],
            ["label"],
            "Plotly",],]

    # 接口
    gyd_img = gr.Interface(
        fn=yolo_det_img,
        inputs=inputs_img_list,
        outputs=outputs_img_list,
        title=title,
        description=description,
        # article=article,
        examples=examples_img,
        # cache_examples=False,
        # theme="seafoam",
        # live=True, # 实时变更输出
        flagging_dir="run",  # 输出目录
        allow_flagging="manual",
        flagging_options=["good", "generally", "bad"],
    )

    gyd_video = gr.Interface(
        fn=yolo_det_video,
        inputs=inputs_video_list,
        outputs=outputs_video_list,
        title=title,
        description=description,
        # article=article,
        examples=examples_video,
        # theme="seafoam",
        # live=True, # 实时变更输出
        flagging_dir="run",  # 输出目录
        allow_flagging="manual",
        flagging_options=["good", "generally", "bad"],
    )

    gyd = gr.TabbedInterface(interface_list=[gyd_img, gyd_video], tab_names=["图片模式", "视频模式"])

    if not is_login:
        gyd.launch(
            inbrowser=True,  # 自动打开默认浏览器
            show_tips=True,  # 自动显示gradio最新功能
            share=is_share,  # 项目共享，其他设备可以访问
            favicon_path="./icon/logo.ico",  # 网页图标
            show_error=True,  # 在浏览器控制台中显示错误信息
            quiet=True,  # 禁止大多数打印语句
        )
    else:
        gyd.launch(
            inbrowser=True,  # 自动打开默认浏览器
            show_tips=True,  # 自动显示gradio最新功能
            auth=usr_pwd,  # 登录界面
            share=is_share,  # 项目共享，其他设备可以访问
            favicon_path="./icon/logo.ico",  # 网页图标
            show_error=True,  # 在浏览器控制台中显示错误信息
            quiet=True,  # 禁止大多数打印语句
        )


if __name__ == "__main__":
    args = parse_args()
    main(args)
